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In today's world, unprecedented amounts of data of individual mobile objects have become more available due to advances in location aware technologies and services. Studying the spatio-temporal patterns, processes, and behavior of mobile objects is an important issue for extracting useful information and knowledge about mobile phenomena. Potential applications across

In today's world, unprecedented amounts of data of individual mobile objects have become more available due to advances in location aware technologies and services. Studying the spatio-temporal patterns, processes, and behavior of mobile objects is an important issue for extracting useful information and knowledge about mobile phenomena. Potential applications across a wide range of fields include urban and transportation planning, Location-Based Services, and logistics. This research is designed to contribute to the existing state-of-the-art in tracking and modeling mobile objects, specifically targeting three challenges in investigating spatio-temporal patterns and processes; 1) a lack of space-time analysis tools; 2) a lack of studies about empirical data analysis and context awareness of mobile objects; and 3) a lack of studies about how to evaluate and test agent-based models of complex mobile phenomena. Three studies are proposed to investigate these challenges; the first study develops an integrated data analysis toolkit for exploration of spatio-temporal patterns and processes of mobile objects; the second study investigates two movement behaviors, 1) theoretical random walks and 2) human movements in urban space collected by GPS; and, the third study contributes to the research challenge of evaluating the form and fit of Agent-Based Models of human movement in urban space. The main contribution of this work is the conceptualization and implementation of a Geographic Knowledge Discovery approach for extracting high-level knowledge from low-level datasets about mobile objects. This allows better understanding of space-time patterns and processes of mobile objects by revealing their complex movement behaviors, interactions, and collective behaviors. In detail, this research proposes a novel analytical framework that integrates time geography, trajectory data mining, and 3D volume visualization. In addition, a toolkit that utilizes the framework is developed and used for investigating theoretical and empirical datasets about mobile objects. The results showed that the framework and the toolkit demonstrate a great capability to identify and visualize clusters of various movement behaviors in space and time.
ContributorsNara, Atsushi (Author) / Torrens, Paul M. (Thesis advisor) / Myint, Soe W (Committee member) / Kuby, Michael (Committee member) / Griffin, William A. (Committee member) / Arizona State University (Publisher)
Created2011
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Description
It is broadly accepted that physical activity provides substantial health benefits. Despite strong evidence, approximately 60% to 95% of US adults are insufficiently active to obtain these health benefits. This dissertation explored five projects that examined the measurement properties and methodology for a variety of physical activity assessment methods. Project

It is broadly accepted that physical activity provides substantial health benefits. Despite strong evidence, approximately 60% to 95% of US adults are insufficiently active to obtain these health benefits. This dissertation explored five projects that examined the measurement properties and methodology for a variety of physical activity assessment methods. Project one identified validity evidence for the new MyWellness Key accelerometer in sixteen adults. The MyWellness Key demonstrated acceptable validity evidence when compared to a criterion accelerometer during graded treadmill walking and in free-living settings. This supports the use of the MyWellness Key accelerometer to measure physical activity. Project two evaluated validity (study 1) and test-retest reliability evidence (study 2) of the Global Physical Activity Questionnaire (GPAQ) in a two part study. The GPAQ was compared to direct and indirect criterion measures including object and subjective physical activity instruments. These data provided preliminary validity and reliability evidence for the GPAQ that support its use to assess physical activity. Project three investigated the optimal h.d-1 of accelerometer wear time needed to assess daily physical activity. Using a semi-simulation approach, data from 124 participants were used to compare 10-13 h.d-1 to the criterion 14 h.d-1. This study suggested that a minimum accelerometer wear time of 13 h.d-1 is needed to provide a valid measure of daily physical activity. Project four evaluated validity and reliability evidence of a novel method (Movement and Activity in Physical Space [MAPS] score) that combines accelerometer and GPS data to assess person-environment interactions. Seventy-five healthy adults wore an accelerometer and GPS receiver for three days to provide MAPS scores. This study provided evidence for use of a MAPS score for future research and clinical use. Project five used accelerometer data from 1,000 participants from the 2005-2006 National Health and Nutrition Examination Study. A semi-simulation approach was used to assess the effect of accelerometer wear time (10-14 h.d-1) on physical activity data. These data showed wearing for 12 h.d-1 or less may underestimate time spent in various intensities of physical activity.
ContributorsHerrmann, Stephen (Author) / Ainsworth, Barbara (Thesis advisor) / Gaesser, Glenn (Committee member) / Der Ananian, Cheryl (Committee member) / Kang, Minsoo (Committee member) / Vega-Lopez, Sonia (Committee member) / Arizona State University (Publisher)
Created2011